基于K-means聚类算法的网络个性化学习行为研究

Analysis and Research of the Network Personalized Learning Behavior Based on K-means Clustering Algorithm

聚类是指按照事物间的相似性对事物进行区分和分类的过程。对网络个性化学习行为中的大量数据,首先对样本数据进行了预处理,然后运用数据挖掘算法中的K-means算法进行分类,获取各类与网络学习行为属性的关系。在Clementine中的实验结果表明,该算法能够将数据准确聚类,为教师教学培养目标的制定提供一定的决策支持。

Clustering is a distinction between things and classification process which refers to things in accordance with the similarity.For numbers of data of the personalized learning behavior on the internet,the paper preprocessed the sample data first,then used the K-means algorithm of data mining classifies the sample data.It can acquire the relationship between each of classification learning behavior of the attributes on the Internet.The experimental results in Clementine show that it can be Clustered accuracy and formulate training objectives provide decision support in teaching process for teachers.